Automated software vulnerability testing using in-depth training methods

Authors

  • Kyrylo Chernov V. N. Karazin Kharkiv National University image/svg+xml
  • Yehor Yeromin V. N. Karazin Kharkiv National University image/svg+xml
  • Popova Mariia V. N. Karazin Kharkiv National University image/svg+xml
  • Shapoval Oleksiy V. N. Karazin Kharkiv National University image/svg+xml
  • Yevgen Kotukh University of Customs and Finance, Dnipro

Keywords:

fuzzing, testing, reinforcement learning, Q-learning

Abstract

Theoretical information about testing of software using fuzzing method. The technologies of reinforcement training and intellectual fuzzing in the software testing process. An algorithm is described with the help of which the indicated methods and technologies are realized. Statistical results of studies that were conducted during the testing of some programs and utilities intended for everyday use, as well as the program developed by the students are offered.

Author Biographies

  • Kyrylo Chernov, V. N. Karazin Kharkiv National University

    computer science student

  • Yehor Yeromin, V. N. Karazin Kharkiv National University

    computer science student

  • Popova Mariia, V. N. Karazin Kharkiv National University

    computer science student

  • Shapoval Oleksiy, V. N. Karazin Kharkiv National University

    computer science student  

  • Yevgen Kotukh, University of Customs and Finance, Dnipro

    Ph.D., Associative professor of the Department Сybersecurity

References

Sutton M., Greene A., Amini P. Fuzzing: Brute Force Vulnerability Discovery. Boston, MA, USA: Addison-Wesley Professional, 2007. URL: https://www.oreilly.com/library/view/fuzzing-brute-force/9780321446114/

AlphaGo Games – English. URL: https://deepmind.com/research/alphago/match-archive/alphago-games-english/

Sutton R.S., Barto A.G. Reinforcement learning: An introduction. MIT press Cambridge, 1998. URL: http://incompleteideas.net/book/bookdraft2017nov5.pdf

Kingma D. P., Ba J. Adam: A Method for Stochastic Optimization. URL: https://arxiv.org/pdf/1412.6980.pdf

t-kryterii Stiudenta. URL: http://fpo.bsmu.edu.ua/static/t-kryteriy-styudenta

Li Yu. Deep Reinforcement Learning: An Overview. URL:https://arxiv.org/pdf/1810.06339.pdf

Böttinger K., Godefroid P., Singh R. Deep Reinforcement Fuzzing. URL: https://arxiv.org/pdf/1801.04589.pdf

Deviniak О. Statystychni hipotezy ta yikh perevirka. 2014. URL: http://stat.org.ua/statclasses/hypotheses-testing/Deviniak Statystychni hipotezy ta yikh perevirka

Published

2019-01-13

Issue

Section

Статті

How to Cite

Automated software vulnerability testing using in-depth training methods. (2019). Computer Science and Cybersecurity, 4, 36-42. https://periodicals.karazin.ua/cscs/article/view/12251

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